Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking

Abstract

The free flow of people and products within metropolitan areas depends on well-managed transportation systems. However, public parking places in smart cities are often limited by traffic, causing cars and residents to waste time, money, and fuel. To counteract this issue, today's automobile systems combine information fusion with intelligent parking solutions. In this research, we present a Fuzzy Logic Integrated Machine Learning Algorithm (FL-MLA) for use in smart parking and traffic management in a metropolis. The FL-MLA use fuzzy induction to distinguish between parked and moving vehicles while calculating traffic flow. The suggested technique efficiently resolves the problem of locating suitable parking places by avoiding incorrect configurations that govern traffic management difficulties. Therefore, the FL-MLA is used in traffic management systems to boost performance metrics like efficiency ratio (98.1%) and accident detection (98.1%) based on simulation results like reduced energy consumption (95.3%), more accurate traffic estimation (97.9%), higher average daily park occupancy (97.2%), and higher efficiency ratio (98.1%).